Objective quality assessment of stereoscopic omnidirectional images is a challenging problem since it is influenced by multiple aspects such as projection deformation, field of view (FoV) range, binocular vision, visual comfort, etc. Existing studies show that classic 2D or 3D image quality assessment (IQA) metrics are not able to perform well for stereoscopic omnidirectional images. However, very few research works have focused on evaluating the perceptual visual quality of omnidirectional images, especially for stereoscopic omnidirectional images. In this article, based on the predictive coding theory of the human vision system (HVS), we propose a stereoscopic omnidirectional image quality evaluator (SOIQE) to cope with the characteristics of 3D 360-degree images. Two modules are involved in SOIQE: predictive coding theory based binocular rivalry module and multi-view fusion module. In the binocular rivalry module, we introduce predictive coding theory to simulate the competition between high-level patterns and calculate the similarity and rivalry dominance to obtain the quality scores of viewport images. Moreover, we develop the multi-view fusion module to aggregate the quality scores of viewport images with the help of both content weight and location weight. The proposed SOIQE is a parametric model without necessary of regression learning, which ensures its interpretability and generalization performance. Experimental results on our published stereoscopic omnidirectional image quality assessment database (SOLID) demonstrate that our proposed SOIQE method outperforms state-of-the-art metrics. Furthermore, we also verify the effectiveness of each proposed module on both public stereoscopic image datasets and panoramic image datasets.

中文翻译：

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基于预测编码理论的立体全方位图像质量评估

立体全向图像的客观质量评估是一个具有挑战性的问题，因为它受到投影变形、视场 (FoV) 范围、双眼视觉、视觉舒适度等多个方面的影响。 现有研究表明，经典的 2D 或 3D 图像质量评估(IQA) 指标不能很好地用于立体全向图像。然而，很少有研究工作专注于评估全向图像的感知视觉质量，尤其是立体全向图像。在本文中，基于人类视觉系统（HVS）的预测编码理论，我们提出了一种立体全方位图像质量评估器（SOIQE）来应对3D 360度图像的特点。SOIQE 涉及两个模块：基于预测编码理论的双目竞争模块和多视图融合模块。在双目竞争模块中，我们引入预测编码理论来模拟高层模式之间的竞争，并计算相似度和竞争优势以获得视口图像的质量分数。此外，我们开发了多视图融合模块，以在内容权重和位置权重的帮助下聚合视口图像的质量分数。所提出的 SOIQE 是一个不需要回归学习的参数模型，这确保了它的可解释性和泛化性能。我们发布的立体全方位图像质量评估数据库 (SOLID) 的实验结果表明，我们提出的 SOIQE 方法优于最先进的指标。此外，